Over the next few months, I’m taking a look at some of the top trends we university leaders must track—trends that will shape our work into the future. In some cases, it’s enough to note and explain the trend; in other cases, we can start looking at ways to respond, meet challenges, and innovate.

In this way, I hope to spark a discussion that can determine policy, procedure, and practice in higher education for years to come. I welcome your comments and ideas on creating the optimum culture for higher education.

A December 2014 New York Times op-ed was designed to capture attention—and it did:

. . . Ball State University in Indiana goes so far as to monitor whether students are swiping in with their ID cards to campus-sponsored parties at the student center on Saturday nights.

The university has taken to heart studies that say that students who are more engaged with college life are also more likely to graduate. When a student’s card-swipe patterns suggest she’s stopped showing up for clubs or socials, a retention specialist will follow up with a call or an email to see how she’s doing. . . .

Big Brother-esque? Perhaps. But these “big data” developments have the potential to cut the cost of higher education for students and their families, as well as for taxpayers.

The piece, by Chronicle of Higher Education senior writer Goldie Blumenstyk, would be repeatedly cited as universities, businesses, and think tanks debated the effects of big data and predictive analytics in higher education—and references to science fiction novels and movies beyond 1984 weren’t in short supply.

Encouraging greater engagement in student life? That’s a great goal, from my perspective. Offering help to students who may be struggling? Again, fantastic.

But targeting certain students, as Ball State did with an app designed for low-income Pell Grant recipients? Or monitoring students’ course attendance, as several universities are—and chasing down those who aren’t showing up?

These are much trickier points, a prompt for us in higher ed to take a step back. We want to make sure big data’s numbers never obscure an institution’s mission—including respect for student privacy. And we must ensure that predictive analytics doesn’t become a self-fulfilling prophecy.

Predictive analytics—in which data and modeling are used to pinpoint what can lead to student success—is behind the idea that students who are more engaged with activities often enjoy better academic and social outcomes.

As for tracking down students who aren’t showing up for class? The University of Maryland tried that, Blumenstyk reports, but there was a slight bug: Students on military reserve duty kept getting warning alerts. The system, the article says, was not “necessarily predictive of academic trouble. The alerts were just annoying.” Obviously, there are many ways to do it wrong.

What are some big-picture considerations for those who want to work well with these new data capacities?

Consideration 1: Culture

Data is changing campus culture in ways large and small. But it’s the current campus culture that will determine how effectively you’ll use data in the future.

Linda Baer, senior fellow at Civitas Learning, formerly a program officer at the Bill & Melinda Gates Foundation and senior administrator at several Minnesota public universities, made it clear when quoted in an article in the Association of Governing Boards of Universities and Colleges Trusteeship magazine:

Baer suggests that the effective use of data analytics pivots on whether a college or university has developed “a culture of inquiry” that is open to mining the full benefits of what data reveals. The right environment, she says, is one that uses numbers and data for continuous improvement. Nurturing that culture, she says, means “training faculty and staff members to understand the insights they can derive from data.”

Just like a blood pressure reading, data may show you things you didn’t know were there, things you didn’t want to see, or things that contradict long-held beliefs. Or—as various “rate my professor” systems have sometimes shown—data can be skewed, irrelevant, even rendered useless through trolling.

Data empowers feedback. Are you ready for the deluge of information? What are your channels and guidelines to ensure you’re harvesting what’s valuable? Can your systems sort out the chaff?

Are you capable, with the right physical and human resources infrastructure, of conducting training so insights can be used? The latter question brings us to a second consideration.

Consideration 2: Infrastructure

Big data and predictive analytics require a stronger sharing and computing capacity than many institutions have had in place. However, we’re seeing more institutions move to cloud computing, where the sky’s the limit.

Technology silos—which keep student life data separate from educational data or undergraduate away from graduate student information—can be required for privacy or other reasons. Yet they can also be a barrier to getting the most value out of data.

Take the admissions process, for example. It’s where many schools have been collecting data for years. That’s a treasure trove for planning student life resources of the future. It tells us what incoming students are looking for and what they need from their college environment.

But how can we best balance privacy needs with desires to effectively use such data?

Consideration 3: Collaboration

Bridget Burns, executive director of the University Innovation Alliance, called out collaboration in her predictive essay in Forbes, exploring how big data could affect higher education:

Collaboration is king. . . . To impact outcomes at scale, universities will need to set aside competition and embrace collaboration. Technology firms will also grapple with collaboration, as they partner with institutional leaders to define the rules of the road for higher education in the digital era. . . .

. . . Sharing best practices, and even de-identified data, will allow institutions to tap into new insights about how to help struggling students. Collaboratives focused on student success are already starting to foster this kind of sharing of best practices.

Accounting for Each Consideration

What do these three considerations have in common? The potential for breaking barriers and forging great new connections for mutual benefit.

To ensure predictive analytics steers us to positive solutions, we need to continue to cultivate a culture receptive to change. To make big data relevant, we need to construct a smart system. And to connect these innovations to student success, we need to collaborate—across departments, across institutions, with businesses, and with nonprofit organizations.